Identifying mechanical vibration modes of a cantilever using spectrally multiplexed Bragg gratings and machine learning
Identifying mechanical vibration modes of a cantilever using spectrally multiplexed Bragg gratings and machine learning
In this paper, we demonstrated the use of the k-Nearest Neighbor, a machine learning algorithm, to identify mechanical vibration modes of a cantilever beam in a frequency range between 40-300 Hz at an accelerations of 1.1±0.1 g. We attached fiber Bragg gratings to the cantilever structure and analyzed the spectral response during vibration. We observe small increases in spectral bandwidth of three Bragg gratings to perform a 3-dimensional classification environment and evaluated the accuracy of the algorithm with independent testing data.
Jantzen, Senta Lisa
e532e171-8ea3-4576-8843-17d96a3995d4
Yu, Jiarui
024ed044-5693-452b-81e8-277837f371bf
Smith, Peter G.R.
8979668a-8b7a-4838-9a74-1a7cfc6665f6
Holmes, Christopher
16306bb8-8a46-4fd7-bb19-a146758e5263
14 July 2020
Jantzen, Senta Lisa
e532e171-8ea3-4576-8843-17d96a3995d4
Yu, Jiarui
024ed044-5693-452b-81e8-277837f371bf
Smith, Peter G.R.
8979668a-8b7a-4838-9a74-1a7cfc6665f6
Holmes, Christopher
16306bb8-8a46-4fd7-bb19-a146758e5263
Jantzen, Senta Lisa, Yu, Jiarui, Smith, Peter G.R. and Holmes, Christopher
(2020)
Identifying mechanical vibration modes of a cantilever using spectrally multiplexed Bragg gratings and machine learning.
In CLEO Pacific Rim.
OSA.
2 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
In this paper, we demonstrated the use of the k-Nearest Neighbor, a machine learning algorithm, to identify mechanical vibration modes of a cantilever beam in a frequency range between 40-300 Hz at an accelerations of 1.1±0.1 g. We attached fiber Bragg gratings to the cantilever structure and analyzed the spectral response during vibration. We observe small increases in spectral bandwidth of three Bragg gratings to perform a 3-dimensional classification environment and evaluated the accuracy of the algorithm with independent testing data.
More information
Published date: 14 July 2020
Venue - Dates:
14th Pacific Rim Conference on Lasers and Electro-Optics (CLEO PR 2020): (Virtual Conference), , Sydney, Australia, 2020-08-03 - 2020-08-05
Identifiers
Local EPrints ID: 442834
URI: http://eprints.soton.ac.uk/id/eprint/442834
PURE UUID: 23edcfb5-d1ab-4ccc-bf6b-8d775387a3b0
Catalogue record
Date deposited: 28 Jul 2020 16:32
Last modified: 17 Mar 2024 05:46
Export record
Contributors
Author:
Senta Lisa Jantzen
Author:
Jiarui Yu
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics